Lie detection and assisted lying
Augmented reality glasses likely will include lie detection applications that monitor people and look for common signs of deception. According to research by Frank Enos of Columbia University, the average person performs worse than chance at detecting lies based on speech patterns and automated systems perform better than chance. Augmented reality can exploit this.
The glasses could conduct voice stress analysis and detect micro-expressions in the target’s face such as eye dilation or blushing. Micro-expressions are very fleeting, occurring in 1/15 of a second, beyond the capabilities of human perception. However, augmented reality systems could detect these fleeting expressions and help determine those attempting to hide the truth. An implication is that people who use this application will become aware of most lies told to them. It could also provide a market for applications that help a person lie.
Gamblers, students, and everyday people will likely use augmented reality to gain an unfair advantage in games of chance or tests of skill. Gamblers could have augmented reality applications that will count cards, assist in following the “money card” in Three Card Monte, or provide real-time odds assessments. Students could use future cheating applications to look at exam questions and immediately see the answers.
Theft and other related crimes may also be facilitated by augmented reality. For example, persistent tagging and change detection could be used to identify homes where the occupants are away on vacation. We anticipate augmented reality will perform at levels above human perception. Applications could notice unlocked cars or windows and alert the potential criminal.
When faced with a new type of security system, the application could suggest techniques to bypass the device, a perverted twist on workplace training. The Google Glass video depicted the user calling up a map to find a desired section of a book store. We anticipate similar applications that might provide escape routes and locations of surveillance cameras.
Law enforcement detection
We also anticipate other applications to support law breaking activities. Today’s radar and laser detectors may feed data into drivers’ glasses as well as collaboratively generated data provided by other drivers about locations of traffic cameras and speed traps. Newer sensors, such as thermal imaging, may allow drivers to see police cars hidden in the bushes a mile down the road. License plate readers and other machine vision approaches will help unmask undercover police cars.
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